1 Statistical Schema Matching across Web Query Interfaces Bin He ， Kevin Chen-Chuan Chang SIGMOD...

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Statistical Schema Matching across Web Query Interfaces

Bin He ， Kevin Chen-Chuan Chang

SIGMOD 2003

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Background: Large-Scale Integration of the deep Web

Query Result

The Deep Web

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Challenge: matching query interfaces (QIs)Book Domain

Music Domain

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Traditional approaches of schema matching – Pairwise Attribute Correspondence Scale is a challenge

Only small scale Large-scale is a

must for our task Scale is an opportunity

Useful ContextPairwise Attribute

Correspondence

S2:writertitlecategoryformat

S3:nametitlekeywordbinding

S1:authortitlesubjectISBN

S1.author S3.nameS1.subject S2.category

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Deep Web Observation

Proliferating sources

Converging

vocabularies

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A hidden schema model exists?

Our View (Hypothesis):

M

P

QIsFinite Vocabulary Statistical Model Generate QIs with different probabilities

QI1

Instantiation probability:P(QI1|M)

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A hidden schema model exists? Our View (Hypothesis):

Now the problem is:

M

P

QIsFinite Vocabulary Statistical Model Generate QIs with different probabilities

P

QIs

Given , can we discover

M

?

QI1

Instantiation probability:P(QI1|M)

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MGS framework & Goal

Hypothesis modeling Hypothesis generation Hypothesis selection

Goal:

Verify the phenomenons

Validate MGSsd with two metrics

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Comparison with Related Work

Related Work Authors’ Work

Paradigms Match two input sources Match many sources

Techniques Machine Learning, Contraint-based, hybrid ones

Statistical approach

Input data Relational or Structured schemas with inconsistency

Interface with consistency

Focuses Name match, structure match,etc

Synonym discovery

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Outline

MGS MGSsd: Hypothesis Modeling, Generation, Selection Deal with Real World Data Final Algorithm Case Study Metrics Experimental Results Conclusion and Future Issues My Assessment

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Towards hidden model discovery: Statistical schema matching (MGS)

1. Define the abstract Model structure M to solve a target question

P(QI|M) = …

M

2. Given QIs, Generate the model candidates

P(QIs|M) > 0

M1 M2

AA BB CC SS TT PP

3. Select the candidate with highest confidence

What is the confidence of given ?

M1

AA BB CC

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MGSSD: Specialize MGS for Synonym Discovery

MGS is generally applicable to a wide range of schema matching tasks E.g., attribute grouping

Focus : discover synonym attributes Author – Writer, Subject – Category No hierarchical matching: Query interface as flat schema No complex matching: (LastName, FirstName) – Author

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Hypothesis Modeling: Structure Goal: capture synonym relationship Two-level model structure

Possible schemas: I1={author, title, subject, ISBN}, I2={title,category, ISBN}

Concepts

Attributes

Mutually Independent

Mutually Exclusive

No overlapping concepts

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Hypothesis Modeling: Formula Definition and Formula:

Probability that M can generate schema I:

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Hypothesis Modeling: Instantiation probability

P(author|M) = α1 * β1P(C1|M)

C1

* P(author|C1) =

author

1.Observing an attribute

2.Observing a schemaP({author, ISBN, subject}|M) = P(author|M) * P(ISBN|M) * P(subject|M) * (1 – P(C2|M))

3.Observing a schema setP(QIs|M) = П P(QIi|M)

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Consistency check

A set of schema I as schema observation <Ii,Bi>:number of occurrences Bi for each Ii M is consistent if Pr (I|M)>0 Find consistent models as candidates

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Hypothesis Generation

Two sub-steps

1. Consistent Concept Construction

2.Build Hypothesis Space

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Hypothesis Generation: Space pruning Prune the space of model candidates

Generate M such that P(QI|M)>0 for any observed QI mutual exclusion assumption Co-occurrence graph

Example: Observations: QI1 = {author, subject} and QI2 = {author, category} Space of model: any set partition of {author, subject, category}

author categorysubject

C1 C3C2

M1


C1 C2

M4


C1C2

M2

author subjectcategory

C1C2

M3


C1

M5

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Hypothesis Generation Prune the space of model candidates

Generate M such that P(QI|M)>0 for any observed QI mutual exclusion assumption

Example: Observations: QI1 = {author, subject} and QI2 = {author, category} Space of model: any set partition of {author, subject, category} Model candidates after pruning:


C1 C3C2

M1


C1 C2

M4


C1C2

M2

author subjectcategory

C1C2

M3


C1

M5

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Hypothesis Generation (Cont.) Build Probability Functions Maximum likelihood estimation

Estimate ai and Bj that maximize Pr (I|M)

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Hypothesis Selection

Rank the model candidates Select the model that generates the closest distribution

to the observations Approach: hypothesis testing

Example: select schema model at significance level 0.05

=3.93 3.93<7.815: accept =20.20 20.20>14.067: reject

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Dealing with the Real World Data Head-often, tail-rare distribution Attribute Selection Systematically remove rare attributes Rare Schema Smoothing Aggregate infrequent schemas into a conceptual event

I(rare) Consensus Projection Follow concept mutual independence assumption

Extract and aggregate New input schemas with re-estimation para.

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Final Algorithm Two phases:

Build initial hypothesis space

Discover the hidden model

Attribute Selection Extract the common parts of model candidates of last iteration

Hypothesis Generation

Hypothesis Selection

Combine rare interfaces

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Experiment Setup in Case Studies Over 200 sources on four domains Threshold f=10% Significance level : 0.05 Can be specified by users

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Example of the MSGsd Algorithm

M1={(ti), (is), (kw), (pr), (fm), (pd), (pu), (su,cg), (au,ln), (fn)}

M2={(ti), (is), (kw), (pr), (fm), (pd), (pu), (su,cg), (au,fn), (ln)}

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Metrics

1. How it is close to the correct schema model Precision: Recall:

2. How good it can answer the target question Precison: Recall:

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Examples on Metrics

I={<I1,6>, <I2,3>, <I3,1>} I1={author, subject}, I2={author, category}, I3={subject} M1={(author:1):0.6, (subject:0.7,category:0.3):1} M2={(author:1):0.6, (subject:1):0.7, (category:1):0.3}

Metrics 1: Pm(M2,Mc)=0.196+0.036+0.249+0.054=0.58 Rm(M2,Mc)=0.28+0.12+0.42+0.18=1

Metrics 2:

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Experimental Results

This approach can identify most concepts correctly Incorrect matchings due to small # observations Do need two suites of metrics Time complexity is exponential

Can generate all correct instances

The discovered synonyms are all correct ones

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Advantages Scalability: large-scale matching Solvability: exploit statistical information Generality

Holistic Model Discovery

author name subject categorywriter




Pairwise Attribute Correspondence




S1.author S3.nameS1.subject S2.category

V.S.

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Conclusions & Future Work

Holistic statistical schema matching of massive sources MGS framework to find synonym attributes Discover hidden models Suited for large-scale database Results verify the observed phenomena and show

accuracy and effectiveness Future Issues

Complex matching: (Last Name, First Name) – Author More efficient approximation algorithm Incorporating other matching techniques

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My Assessments Promise

Use minimal “light-weight” information: attribute name

Effective with sufficient instances Leverage challenge as opportunity

Limitation Need sufficient observations Simple Assumptions Exponential time complexity Homonyms

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Questions

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